Cumulative Clonal Growth Rates
Growth rate, 𝛼 = log (𝑉𝐴𝐹 𝑓𝑜𝑙𝑙𝑜𝑤-𝑢𝑝/𝑉𝐴𝐹 𝑏𝑎𝑠𝑒𝑙𝑖𝑛𝑒 )/𝑡𝑖𝑚𝑒 Patients 14
and 96 have no VAF info because they did not have nay VAFS, therefore
their growth rate is 0
patients being removed from VAF analysis due to no follow-up
visit
| 17 |
1 |
Baseline |
43174 |
17 |
1 |
| 21 |
1 |
Baseline |
43243 |
21 |
1 |
| 32 |
1 |
Baseline |
43502 |
32 |
1 |
| 43 |
1 |
Baseline |
43679 |
43 |
1 |
| 48 |
1 |
Baseline |
43781 |
48 |
1 |
| 76 |
1 |
Baseline |
44088 |
76 |
1 |
| 82 |
1 |
Baseline |
44091 |
82 |
1 |
| 87 |
1 |
Baseline |
44266 |
87 |
1 |
| USC-60-1 |
1 |
Baseline |
44550 |
USC60 |
1 |
Cumulative
All patients
Accounting: 109 patients, 9 with only a follow-up time, 3 without any
VAF info, and 1 without either for a total of 11 patients excluded (8 no
follow-up only, 2 no VAFS info only, 1 missing both)
|
Placebo (N=51) |
Vitamin C (N=49) |
Overall (N=100) |
| GR |
|
|
|
| Mean (SD) |
0.843 (3.25) |
0.450 (1.98) |
0.650 (2.70) |
| Median [Min, Max] |
0.201 [-2.70, 18.4] |
0.0404 [-4.69, 9.66] |
0.111 [-4.69, 18.4] |
emmeans and contrast
| Placebo |
0.3057725 |
0.1484876 |
NA |
0.0147422 |
0.5968028 |
| Vitamin C |
0.1964945 |
0.1514876 |
NA |
-0.1004157 |
0.4934048 |
|
| Placebo - Vitamin C |
0.109278 |
0.2121251 |
NA |
-0.3064795 |
0.5250355 |
|
p-value on difference
| Treatment_arm |
1 |
Inf |
0.265 |
0.265 |
0.6064424 |

FC
| Placebo |
0.3736240 |
0.1075241 |
NA |
0.2114759 |
0.6544847 |
| Vitamin C |
0.5695938 |
0.1867436 |
NA |
0.2982257 |
1.0798801 |
|
| Placebo - Vitamin C |
-0.5953494 |
0.6161018 |
NA |
-1.802887 |
0.6121879 |
|
Subgroups
TET2 mutation status

Baseline diagnosis

Individual mutations
##
## FLT3 GNB1 NRAS PHF6 PPM1D SETBP1 BCOR BRAF ETNK1 ETV6 GATA2
## 1 1 1 1 1 1 2 2 2 2 2
## JAK2 KIT MPL CEBPA EZH2 IDH2 NF1 PTPN11 IDH1 CBL KRAS
## 2 2 2 3 3 3 3 3 4 6 6
## SH2B3 STAG2 TP53 U2AF1 RUNX1 SF3B1 ASXL1 ZRSR2 SRSF2 DNMT3A TET2
## 6 7 7 7 14 21 24 26 27 31 108
TET2

DNMT3A

SRSF2

ASXL1

ZRSR2

Emergence of a new mutation
| Placebo |
-1.085708 |
0.3263456 |
Inf |
-1.725334 |
-0.4460830 |
| Vitamin C |
-1.114014 |
0.3354149 |
Inf |
-1.771416 |
-0.4566132 |
|
| Placebo - Vitamin C |
0.0283058 |
0.4734656 |
Inf |
0.0597842 |
0.9523275 |
|
| Treatment_arm |
1 |
Inf |
0.004 |
0.004 |
0.9523275 |
| time |
1 |
Inf |
0.063 |
0.063 |
0.8010501 |
|
Placebo (N=51) |
Vitamin C (N=49) |
Overall (N=100) |
| Emergent |
|
|
|
| Mean (SD) |
0.255 (0.440) |
0.245 (0.434) |
0.250 (0.435) |
| Median [Min, Max] |
0 [0, 1.00] |
0 [0, 1.00] |
0 [0, 1.00] |
| time |
|
|
|
| Mean (SD) |
0.920 (0.296) |
1.03 (0.181) |
0.972 (0.251) |
| Median [Min, Max] |
1.02 [0.235, 1.43] |
1.02 [0.249, 1.33] |
1.02 [0.235, 1.43] |
Progress, stable, Contracting clones
Each defined as: averaging >10% over 1 year is an expanding
clones, <10% contracting clones, within +/-10% stable.

Plasma Vitamin C
Correlate Vitamin C in plasma and bone marrow

Survival
Includes all samples, unadjusted. We also performed a landmark
analysis and also analyzed this same data with peto-peto. Both were
still significant, therefore an FDR correction would not change our
findings here.
Cox model (No interaction)
Supplemntal figure XX. Forest plot for adjusted Cox
model. Cox proportional hazards regression to estimated
efficacy of vitamin C on overall survival was adjusted for patient age
at inclusion, gender, hemoglobin levels at inclusion, and whether or not
they had been diagnosed with CCUS.

Cox model (Age interaction)
Supplemental figure XX. Forest plot for adjusted Cox model in
age interaction. Cox proportional hazards regression to
estimated efficacy of vitamin C as a function of age. Age was mean
centered and converted to decades to improve hazard ratio scales.
Overall vitamin C was still effective, with evidence that it’s efficacy
decreases with age.

Cox model (TET interaction)
| TET2Mut |
32 |
31 |
| TET2neg |
22 |
24 |

Cox model (Hb1 interaction)

Cox model (Sex interaction)

Cox model (CCUS interaction)
Forest plot cannot be made due to diverging SE in treatment
## Call:
## coxph(formula = Surv(Days_FU_OS, FU_Death) ~ VitaminC + age_c +
## vitc_ccus + Male + Hb1_c + CCUS, data = df)
##
## coef exp(coef) se(coef) z p
## VitaminC -0.48013788583 0.61869807622 0.39738120115 -1.208 0.226949
## age_c 0.04289957950 1.04383306735 0.02439246075 1.759 0.078625
## vitc_ccus -18.85453900918 0.00000000648 5424.03021304527 -0.003 0.997226
## Male 0.84285200671 2.32298270049 0.46551028687 1.811 0.070203
## Hb1_c -0.67791720098 0.50767327337 0.18565236465 -3.652 0.000261
## CCUS 0.23284559292 1.26218657373 0.51029109759 0.456 0.648175
##
## Likelihood ratio test=40.98 on 6 df, p=0.0000002919
## n= 109, number of events= 35
Cox model (baseline vitamin C interaction)

2-year landmark
Maybe Figure? A 2 year landmark analysis was
performed to determine if there was still evidence of vitamin C efficacy
after removing individuals who had events prior to 2 years. This
landmark analysis ensures the effects of vitamin C had sufficient time
to reach efficacy.

Per patient protocol
likelihood ratio test p-value is nearly identical to log-rank

LRT on treatment
| 1 |
-76.00080 |
NA |
NA |
NA |
| 0 |
-77.16977 |
-1 |
2.337937 |
0.1262567 |
Subgroups (suppl)
All subgroup analyses include only the hazard ratio with 95% CI as
these are exploratory.
TET1 or IDH1/2 at baseline
Supplemental Figure XX. Overall survival by TET2 or IDH1/2
mutation status. Hazard ratio and confidence intervals were
estimated using Cox proportional hazards models on data stratified by
TET2 and IDH1/2 mutation status. Survival curves and estimates are
consistent with the primary outcome.
LRT for interaction
| 3 |
-142.8280 |
NA |
NA |
NA |
| 2 |
-143.5895 |
-1 |
1.523109 |
0.2171495 |

Competing risk regression (progression)
For this analysis death is a competing risk of high-risk progression.
We are a bit limited in sample size and events; there are only 14
progression events. Power to detect any differences in progression is
quite low and we will have difficulty adjusting for any covariates here.
We see corroboration with out overall survival findings that death (the
dotted lines) are significantly different, however we do not have enough
evidence to determine if progression also differs (p = 0.22).
Because competing risks comes from a different package, I can’t seem
to add a y-axis title to the risk table

Global Methylation
Figure XX. Global methylation changes over time by treatment
arm A) 5-mC/dG and B) 5-hmC/5-mC level for each patient at
inclusion and end-of-therapy (EOT) and the change in these measures
between inclusion and EOT. Data were analyzed using robust linear
mixed-effects models with a random intercept for each patient and an
interaction between treatment and time point to assess if measures
changed differently over time. Models also included a covariate for
batch.
IDs not in Mass Spec data
| 75 |
| 79 |
| 6 |
| 5 |
| 4 |
| 26 |
| 47 |
| 23 |
| 93 |
IDs not in methylation data
| 12 |
| 17 |
| 21 |
| 43 |
| 48 |
| 76 |
| USC52 |
| USC54 |
| USC57 |
| 32 |
| 87 |
| USC53 |
| USC59 |
Delta 5-mC and 5-hmC/5-mC
TET2

baseline vitamin C

Diagnosis

Baseline 5-mC and 5-hmC/5-mC
The raw p-values should be printing in each section
Saved p-values and FDR adjustments
| mC.dG |
TET2 |
0.1991000 |
0.2844286 |
| mC.dG |
vitc_med |
0.1066000 |
0.2132000 |
| mC.dG |
vitc_ccus |
0.0078000 |
0.0390000 |
| mC.dG |
vitc_ccus |
0.6186000 |
0.6873333 |
| mC.dG |
vitc_ccus |
0.0797000 |
0.2092500 |
| hmC.mC |
TET2 |
0.0000001 |
0.0000009 |
| hmC.mC |
vitc_med |
0.5790000 |
0.6873333 |
| hmC.mC |
vitc_ccus |
0.7838000 |
0.7838000 |
| hmC.mC |
vitc_ccus |
0.1405000 |
0.2341667 |
| hmC.mC |
vitc_ccus |
0.0837000 |
0.2092500 |
TET2
## contrast estimate SE df
## (No TET2 or IDH1/2 mutation) - (TET2 or IDH1/2 mutation) -0.000977 0.000761 NA
## z.ratio p.value
## -1.284 0.1991
##
## Results are averaged over the levels of: Treatment_arm
## Note: contrasts are still on the as.numeric scale. Consider using
## regrid() if you want contrasts of back-transformed estimates.
## contrast estimate SE df
## (No TET2 or IDH1/2 mutation) - (TET2 or IDH1/2 mutation) 0.00285 0.000533 NA
## z.ratio p.value
## 5.349 <.0001
##
## Results are averaged over the levels of: Treatment_arm
## Note: contrasts are still on the as.numeric scale. Consider using
## regrid() if you want contrasts of back-transformed estimates.

baseline vitamin C
## contrast estimate SE df z.ratio p.value
## Below median - Above median -0.00119 0.000737 NA -1.614 0.1066
##
## Results are averaged over the levels of: Treatment_arm
## Note: contrasts are still on the as.numeric scale. Consider using
## regrid() if you want contrasts of back-transformed estimates.
## contrast estimate SE df z.ratio p.value
## Below median - Above median -0.000329 0.000593 NA -0.555 0.5790
##
## Results are averaged over the levels of: Treatment_arm
## Note: contrasts are still on the as.numeric scale. Consider using
## regrid() if you want contrasts of back-transformed estimates.

Diagnosis
## contrast estimate SE df z.ratio p.value
## CCUS - MDS 0.002199 0.000827 NA 2.659 0.0078
## CCUS - (MDS/MPN) 0.000492 0.000989 NA 0.498 0.6186
## MDS - (MDS/MPN) -0.001707 0.000974 NA -1.752 0.0797
##
## Results are averaged over the levels of: Treatment_arm
## Note: contrasts are still on the as.numeric scale. Consider using
## regrid() if you want contrasts of back-transformed estimates.
## contrast estimate SE df z.ratio p.value
## CCUS - MDS -0.000178 0.000649 NA -0.274 0.7838
## CCUS - (MDS/MPN) 0.001144 0.000776 NA 1.474 0.1405
## MDS - (MDS/MPN) 0.001322 0.000764 NA 1.730 0.0837
##
## Results are averaged over the levels of: Treatment_arm
## Note: contrasts are still on the as.numeric scale. Consider using
## regrid() if you want contrasts of back-transformed estimates.

5-mC/dG
All corr
##
## All
## Placebo 45
## Vitamin C 40

5-hmC/dG
All corr
##
## All
## Placebo 45
## Vitamin C 40

5-hmC/5-mC
All corr
##
## All
## Placebo 45
## Vitamin C 40
